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The Analytic Hierarchy Process (AHP) has been one of the foremost mathematical methods for decision making with multiple criteria and has been widely studied in the operations research literature as well as applied to solve countless real-world problems. This book is meant to introduce and strengthen the readers’ knowledge of the AHP, no matter how familiar they may be with the topic. This book provides a concise, yet self-contained, introduction to the AHP that uses a novel and more pedagogical approach. It begins with an introduction to the principles of the AHP, covering the critical points of the method, as well as some of its applications. Next, the book explores further aspects of the method, including the derivation of the priority vector, the estimation of inconsistency, and the use of AHP for group decisions. Each of these is introduced by relaxing initial assumptions. Furthermore, this booklet covers extensions of AHP, which are typically neglected in elementary expositions of the methods. Such extensions concern different numerical representations of preferences and the interval and fuzzy representations of preferences to account for uncertainty. During the whole exposition, an eye is kept on the most recent developments of the method.
We are pleased to welcome readers to this issue of the Journal of Applied Operational Research (JAOR), Volume 7, Number 1. The journal reports on developments in all aspects of operational research, including the latest advances and applications. It is a primarily goal of the journal to focus on and publish practical case studies which illustrate real-life applications.
Economists, decision analysts, management scientists, and others have long argued that government should take a more scientific approach to decision making. Pointing to various theories for prescribing and rational izing choices, they have maintained that social goals could be achieved more effectively and at lower costs if government decisions were routinely subjected to analysis. Now, government policy makers are putting decision science to the test. Recent government actions encourage and in some cases require government decisions to be evaluated using formally defined principles 01' rationality. Will decision science pass tbis test? The answer depends on whether analysts can quickly and successfully translate their theories into practical approaches and whether these approaches promote the solution of the complex, highly uncertain, and politically sensitive problems that are of greatest concern to government decision makers. The future of decision science, perhaps even the nation's well-being, depends on the outcome. A major difficulty for the analysts who are being called upon by government to apply decision-aiding approaches is that decision science has not yet evolved a universally accepted methodology for analyzing social decisions involving risk. Numerous approaches have been proposed, including variations of cost-benefit analysis, decision analysis, and applied social welfare theory. Each of these, however, has its limitations and deficiencies and none has a proven track record for application to govern ment decisions involving risk. Cost-benefit approaches have been exten sively applied by the government, but most applications have been for decisions that were largely risk-free.
These proceedings gather contributions presented at the 3rd International Conference on Applied Operational Research (ICAOR 2011) in Istanbul, Turkey, August 24-26, 2011, published in the series Lecture Notes in Management Science (LNMS). The conference covers all aspects of Operational Research and Management Science (OR/MS) with a particular emphasis on applications.
Confounding Medical Consensus, an 81-year-old hatha yoga master, whom a surgical error rendered a quasi-quadriplegist, and who was told independently by three doctors that he would never walk again, WALKS in 8 Months! A Triumph of Therapy; A Triumph of Faith! Until I directly confronted the Health Care Provider Medical Director at the Hebrew Home, for 10 weeks I was flailing against lengthening shadows of ignorance of my truly hopeless condition because my surgeon chose to violate his legal requirement to tell me my physical condition. Is this affliction a punishment from God? Can I, who have delivered many Prophetic Messages to meet many concrete needs of many people at four different churches in Asia and America over 60 years, ask God for a Prophecy of Healing? Will God find me Faithless if I don't take literally Prophecy I &/Or II regarding my healing? In the second week of my stay at Hebrew Home of Greater Washington, D.C, the Health Care Provider Medical Director decided to discharge me around September 30. Two fundamental miracles had to occur to void the imminent discharge. I sketch the Herculean efforts to re-create 23 functionalities beginning with the likes of crawling: (1) Crawl -> (2) Kneel -> (3) Scoot -> (4) Stand -> (5) Transfer -> (6) Walk. The re-creation of functionalities takes place within the certainty of the date of my death, known since 1979 at age 49, which assures me that the quadriplegia is not unto death. I have confounded medical consensus that I will never walk again, thanks to the faith of several communities continually praying for my recovery-the faith creating and consummating the miracle of a quasi-quadriplegist walking in 8 months, rapidly progressing to walking 3 miles a day unassisted.
He consider a cone dominance problem: given a "preference" cone lP and a set n X ~ R of available, or feasible, alternatives, the problem is to identify the non dominated elements of X. The nonzero elements of lP are assumed to model the do- nance structure of the problem so that y s X dominates x s X if Y = x + P for some nonzero p S lP. Consequently, x S X is nondominated if, and only if, ({x} + lP) n X = {x} (1.1) He will also refer to nondominated points as efficient points (in X with respect to lP) and we will let EF(XJP) denote the set of such efficient points. This cone dominance problem draws its roots from two separate, but related, ori gins. The first of these is multi-attribute decision making in which the elements of the set X are endowed with various attributes, each to be maximized or minimized.